app.py

import gradio as gr
from transformers import (
    pipeline,
    AutoProcessor,
    AutoModelForCausalLM,
    AutoTokenizer,
    GenerationConfig,
    set_seed
)
from datasets import load_dataset
import torch
import numpy as np

set_seed(42)

# Device and dtype
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Load image captioning model
caption_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")

# Load SpeechT5 text-to-speech model
synthesiser = pipeline("text-to-speech", model="microsoft/speecht5_tts")

# Load OCR model (Florence-2)
ocr_model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Florence-2-large", torch_dtype=dtype, trust_remote_code=True
).to(device)
ocr_processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)

# Load Doge-320M-Instruct for context generation
doge_tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-320M-Instruct")
doge_model = AutoModelForCausalLM.from_pretrained(
    "SmallDoge/Doge-320M-Instruct", trust_remote_code=True
).to(device)
doge_generation_config = GenerationConfig(
    max_new_tokens=100,
    use_cache=True,
    do_sample=True,
    temperature=0.8,
    top_p=0.9,
    repetition_penalty=1.0
)

# Load speaker embedding with fallback
speaker_embedding = None
embedding_data = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")

for entry in embedding_data:
    vec = entry["xvector"]
    if len(vec) >= 600:
        speaker_embedding = torch.tensor(vec[:600], dtype=torch.float32).unsqueeze(0)
        break

# Fallback: use a zero vector if none found
if speaker_embedding is None:
    print("⚠️ No suitable speaker embedding found. Using default 600-dim zero vector.")
    speaker_embedding = torch.zeros(1, 600, dtype=torch.float32)

# Ensure correct shape
assert speaker_embedding.shape == (1, 600), f"Expected shape (1, 600), got {speaker_embedding.shape}"


def process_image(image):
    try:
        # 1. Caption the image
        caption = caption_model(image)[0]['generated_text']

        # 2. OCR with Florence-2
        inputs = ocr_processor(text="<OCR>", images=image, return_tensors="pt").to(device, dtype)
        generated_ids = ocr_model.generate(
            input_ids=inputs["input_ids"],
            pixel_values=inputs["pixel_values"],
            max_new_tokens=4096,
            num_beams=3,
            do_sample=False
        )
        extracted_text = ocr_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

        # 3. Prompt Doge model for context generation
        prompt = f"Determine the context of this image based on the caption and extracted text.\nCaption: {caption}\nExtracted text: {extracted_text}\nContext:"
        prompt = prompt[:600]  # Prevent tensor mismatch error
        conversation = [{"role": "user", "content": prompt}]
        doge_inputs = doge_tokenizer.apply_chat_template(
            conversation=conversation,
            tokenize=True,
            return_tensors="pt"
        ).to(device)

        doge_output = doge_model.generate(
            input_ids=doge_inputs,
            generation_config=doge_generation_config
        )
        context = doge_tokenizer.decode(doge_output[0], skip_special_tokens=True).strip()

        # 4. Convert context to speech
        speech = synthesiser(
            context,
            forward_params={"speaker_embeddings": speaker_embedding}
        )

        audio = np.array(speech["audio"])
        rate = speech["sampling_rate"]

        return (rate, audio), caption, extracted_text, context

    except Exception as e:
        return None, f"Error: {str(e)}", "", ""


# Gradio Interface
iface = gr.Interface(
    fn=process_image,
    inputs=gr.Image(type='pil', label="Upload an Image"),
    outputs=[
        gr.Audio(label="Generated Audio"),
        gr.Textbox(label="Generated Caption"),
        gr.Textbox(label="Extracted Text (OCR)"),
        gr.Textbox(label="Generated Context")
    ],
    title="SeeSay Contextualizer with Doge-320M",
    description="Upload an image to caption it, extract text, generate context, and hear the result as speech."
)

iface.launch(share=True)